# SciRisk-Bench: A Risk-Dimension-Aware Benchmark for AI4Science Safety

arXiv: https://arxiv.org/abs/2606.18936 · HTML: https://mlsanity.com/paper/2606.18936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning](https://mlsanity.com/paper/2403.03218/) (2024)
- [SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research](https://mlsanity.com/paper/2308.13149/) (2023)
- [SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents](https://mlsanity.com/paper/2505.23559/) (2025)
- [SMILES-Prompting: A Novel Approach to LLM Jailbreak Attacks in Chemical Synthesis](https://mlsanity.com/paper/2410.15641/) (2024)

## Built on by (most cited first, 1 of 1)

- [SafeMol: Dual-Modality Safety Alignment for Molecular Multimodal Models](https://mlsanity.com/paper/2609.33640/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly embedded in AI for Science (AI4Science) workflows, from scientific question answering and literature analysis to laboratory planning and autonomous discovery. This progress creates an urgent need for safety benchmarks that evaluate not only scientific competence, but also whether models recognize and avoid risks in high-stakes scientific contexts. Existing AI4Science safety datasets cover several disciplines and task formats, leaving the underlying risk dimensions underspecified. We introduce SciRisk-Bench, a benchmark designed to evaluate AI4Science safety from two complementary perspectives: explicit risk dimensions and scientific disciplines. SciRisk-Bench covers 7 disciplines, 31 subdisciplines and 10 risk dimensions. In the experimental section, we evaluate both mainstream LLMs and science-oriented LLMs across risk dimensions, disciplines, and sub-disciplines, enabling fine-grained diagnosis of where scientific models remain unsafe.